Overview

Brought to you by YData

Dataset statistics

 Profil - Données OriginalesProfil - Données Dérivées
Number of variables88
Number of observations1000000010000000
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory3.5 GiB3.5 GiB
Average record size in memory370.9 B370.9 B

Variable types

 Profil - Données OriginalesProfil - Données Dérivées
Numeric33
Text11
Categorical33
DateTime11

Alerts

Profil - Données OriginalesProfil - Données Dérivées
purchase_id has unique values purchase_id has unique values Unique

Reproduction

 Profil - Données OriginalesProfil - Données Dérivées
Analysis started2025-08-04 16:08:55.5878862025-08-04 16:16:49.452099
Analysis finished2025-08-04 16:16:49.4228412025-08-04 16:48:33.873773
Duration7 minutes and 53.83 seconds31 minutes and 44.42 seconds
Software versionydata-profiling vv4.16.1ydata-profiling vv4.16.1
Download configurationconfig.jsonconfig.json

Variables

customer_id
Real number (ℝ)

 Profil - Données OriginalesProfil - Données Dérivées
Distinct89998999
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5499.60375499.6037
 Profil - Données OriginalesProfil - Données Dérivées
Minimum10001000
Maximum99989998
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size76.3 MiB76.3 MiB
2025-08-04T17:49:26.116879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 Profil - Données OriginalesProfil - Données Dérivées
Minimum10001000
5-th percentile14511451
Q132503250
median55005500
Q377497749
95-th percentile95489548
Maximum99989998
Range89988998
Interquartile range (IQR)44994499

Descriptive statistics

 Profil - Données OriginalesProfil - Données Dérivées
Standard deviation2597.48232597.4823
Coefficient of variation (CV)0.472303540.47230354
Kurtosis-1.1999627-1.1999627
Mean5499.60375499.6037
Median Absolute Deviation (MAD)22502250
Skewness-9.9463172 × 10-5-9.9463172 × 10-5
Sum5.4996037 × 10105.4996037 × 1010
Variance6746914.56746914.5
MonotonicityNot monotonicNot monotonic
2025-08-04T17:49:27.304282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3640 1228
 
< 0.1%
2948 1221
 
< 0.1%
1692 1220
 
< 0.1%
1926 1219
 
< 0.1%
1724 1219
 
< 0.1%
1623 1218
 
< 0.1%
1637 1216
 
< 0.1%
9879 1216
 
< 0.1%
5020 1216
 
< 0.1%
2164 1215
 
< 0.1%
Other values (8989) 9987812
99.9%
ValueCountFrequency (%)
3640 1228
 
< 0.1%
2948 1221
 
< 0.1%
1692 1220
 
< 0.1%
1926 1219
 
< 0.1%
1724 1219
 
< 0.1%
1623 1218
 
< 0.1%
1637 1216
 
< 0.1%
9879 1216
 
< 0.1%
5020 1216
 
< 0.1%
2164 1215
 
< 0.1%
Other values (8989) 9987812
99.9%
ValueCountFrequency (%)
1000 1135
< 0.1%
1001 1089
< 0.1%
1002 1089
< 0.1%
1003 1105
< 0.1%
1004 1079
< 0.1%
1005 1097
< 0.1%
1006 1117
< 0.1%
1007 1083
< 0.1%
1008 1126
< 0.1%
1009 1097
< 0.1%
ValueCountFrequency (%)
1000 1135
< 0.1%
1001 1089
< 0.1%
1002 1089
< 0.1%
1003 1105
< 0.1%
1004 1079
< 0.1%
1005 1097
< 0.1%
1006 1117
< 0.1%
1007 1083
< 0.1%
1008 1126
< 0.1%
1009 1097
< 0.1%
ValueCountFrequency (%)
1000 1135
< 0.1%
1001 1089
< 0.1%
1002 1089
< 0.1%
1003 1105
< 0.1%
1004 1079
< 0.1%
1005 1097
< 0.1%
1006 1117
< 0.1%
1007 1083
< 0.1%
1008 1126
< 0.1%
1009 1097
< 0.1%
ValueCountFrequency (%)
1000 1135
< 0.1%
1001 1089
< 0.1%
1002 1089
< 0.1%
1003 1105
< 0.1%
1004 1079
< 0.1%
1005 1097
< 0.1%
1006 1117
< 0.1%
1007 1083
< 0.1%
1008 1126
< 0.1%
1009 1097
< 0.1%

purchase_id
['Text', 'Text']

 Profil - Données OriginalesProfil - Données Dérivées
Distinct1000000010000000
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Memory size886.9 MiB886.9 MiB
2025-08-04T17:49:54.707694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

 Profil - Données OriginalesProfil - Données Dérivées
Max length3636
Median length3636
Mean length3636
Min length3636

Characters and Unicode

 Profil - Données OriginalesProfil - Données Dérivées
Total characters360000000360000000
Distinct characters1717
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Profil - Données OriginalesProfil - Données Dérivées
Unique1000000010000000 ?
Unique (%)100.0%100.0%

Sample

 Profil - Données OriginalesProfil - Données Dérivées
1st rowbdd640fb-0667-4ad1-9c80-317fa3b1799dbdd640fb-0667-4ad1-9c80-317fa3b1799d
2nd row23b8c1e9-3924-46de-beb1-3b904668525723b8c1e9-3924-46de-beb1-3b9046685257
3rd rowbd9c66b3-ad3c-4d6d-9a3d-1fa7bc8960a9bd9c66b3-ad3c-4d6d-9a3d-1fa7bc8960a9
4th row972a8469-1641-4f82-8b9d-2434e465e150972a8469-1641-4f82-8b9d-2434e465e150
5th row17fc695a-07a0-4a6e-8822-e8f36c03119917fc695a-07a0-4a6e-8822-e8f36c031199
ValueCountFrequency (%)
c6978940-e936-4971-9859-3f9587cec5fa 1
 
< 0.1%
f1ab10a8-af22-4d47-9b1d-b4aa95773946 1
 
< 0.1%
92c865db-3fe8-4257-96cd-706485b7c578 1
 
< 0.1%
bc9a436a-2df1-4171-8ada-81142d92b032 1
 
< 0.1%
21a8d0ce-164d-4b9c-921b-86a687f35ca5 1
 
< 0.1%
fb34a115-e0df-45ac-bc8b-f9cb8012cfcf 1
 
< 0.1%
1ffb126f-5897-480e-b839-3a8d0035accd 1
 
< 0.1%
17c3e766-bf4d-4128-a509-11f4ee011ca0 1
 
< 0.1%
8cc4aa8f-e04b-4751-aa6f-db0f28eef8ae 1
 
< 0.1%
9da02928-aa42-4d2d-b6ac-803f4e6e9f4c 1
 
< 0.1%
Other values (9999990) 9999990
> 99.9%
ValueCountFrequency (%)
c6978940-e936-4971-9859-3f9587cec5fa 1
 
< 0.1%
f1ab10a8-af22-4d47-9b1d-b4aa95773946 1
 
< 0.1%
92c865db-3fe8-4257-96cd-706485b7c578 1
 
< 0.1%
bc9a436a-2df1-4171-8ada-81142d92b032 1
 
< 0.1%
21a8d0ce-164d-4b9c-921b-86a687f35ca5 1
 
< 0.1%
fb34a115-e0df-45ac-bc8b-f9cb8012cfcf 1
 
< 0.1%
1ffb126f-5897-480e-b839-3a8d0035accd 1
 
< 0.1%
17c3e766-bf4d-4128-a509-11f4ee011ca0 1
 
< 0.1%
8cc4aa8f-e04b-4751-aa6f-db0f28eef8ae 1
 
< 0.1%
9da02928-aa42-4d2d-b6ac-803f4e6e9f4c 1
 
< 0.1%
Other values (9999990) 9999990
> 99.9%
2025-08-04T17:50:21.676315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360000000
100.0%
ValueCountFrequency (%)
(unknown) 360000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360000000
100.0%
ValueCountFrequency (%)
(unknown) 360000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360000000
100.0%
ValueCountFrequency (%)
(unknown) 360000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%
ValueCountFrequency (%)
- 40000000
 
11.1%
4 28753749
 
8.0%
a 21256025
 
5.9%
9 21253184
 
5.9%
b 21252535
 
5.9%
8 21246418
 
5.9%
0 18753429
 
5.2%
f 18752795
 
5.2%
2 18752438
 
5.2%
e 18752331
 
5.2%
Other values (7) 131227096
36.5%

product_category
Categorical

 Profil - Données OriginalesProfil - Données Dérivées
Distinct55
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size612.3 MiB612.3 MiB
fashion
2001425 
grocery
2000108 
electronics
1999725 
sports
1999442 
books
1999300 
fashion
2001425 
grocery
2000108 
electronics
1999725 
sports
1999442 
books
1999300 

Length

 Profil - Données OriginalesProfil - Données Dérivées
Max length1111
Median length77
Mean length7.20008587.2000858
Min length55

Characters and Unicode

 Profil - Données OriginalesProfil - Données Dérivées
Total characters7200085872000858
Distinct characters1717
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Profil - Données OriginalesProfil - Données Dérivées
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Profil - Données OriginalesProfil - Données Dérivées
1st rowelectronicselectronics
2nd rowfashionfashion
3rd rowbooksbooks
4th rowbooksbooks
5th rowsportssports

Common Values

ValueCountFrequency (%)
fashion 2001425
20.0%
grocery 2000108
20.0%
electronics 1999725
20.0%
sports 1999442
20.0%
books 1999300
20.0%
ValueCountFrequency (%)
fashion 2001425
20.0%
grocery 2000108
20.0%
electronics 1999725
20.0%
sports 1999442
20.0%
books 1999300
20.0%

Length

2025-08-04T17:50:21.936038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Profil - Données Originales

2025-08-04T17:50:22.204556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:50:22.473557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fashion 2001425
20.0%
grocery 2000108
20.0%
electronics 1999725
20.0%
sports 1999442
20.0%
books 1999300
20.0%
ValueCountFrequency (%)
fashion 2001425
20.0%
grocery 2000108
20.0%
electronics 1999725
20.0%
sports 1999442
20.0%
books 1999300
20.0%

Most occurring characters

ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72000858
100.0%
ValueCountFrequency (%)
(unknown) 72000858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72000858
100.0%
ValueCountFrequency (%)
(unknown) 72000858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72000858
100.0%
ValueCountFrequency (%)
(unknown) 72000858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%
ValueCountFrequency (%)
o 11999300
16.7%
s 9999334
13.9%
r 7999383
11.1%
e 5999558
8.3%
c 5999558
8.3%
n 4001150
 
5.6%
i 4001150
 
5.6%
t 3999167
 
5.6%
f 2001425
 
2.8%
a 2001425
 
2.8%
Other values (7) 13999408
19.4%

purchase_amount
Real number (ℝ)

 Profil - Données OriginalesProfil - Données Dérivées
Distinct3328733287
Distinct (%)0.3%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean60.00209290.003137
 Profil - Données OriginalesProfil - Données Dérivées
Minimum0.010.015
Maximum690.431035.645
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size76.3 MiB76.3 MiB
2025-08-04T17:50:23.006115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 Profil - Données OriginalesProfil - Données Dérivées
Minimum0.010.015
5-th percentile10.6615.99
Q128.8343.245
median50.3575.525
Q380.77121.155
95-th percentile142.41213.615
Maximum690.431035.645
Range690.421035.63
Interquartile range (IQR)51.9477.91

Descriptive statistics

 Profil - Données OriginalesProfil - Données Dérivées
Standard deviation42.44986963.674803
Coefficient of variation (CV)0.707473150.70747315
Kurtosis3.01473333.0147333
Mean60.00209290.003137
Median Absolute Deviation (MAD)24.636.9
Skewness1.4165291.416529
Sum6.0002092 × 1089.0003137 × 108
Variance1801.99144054.4806
MonotonicityNot monotonicNot monotonic
2025-08-04T17:50:23.492924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.1 1337
 
< 0.1%
31.79 1331
 
< 0.1%
33.36 1325
 
< 0.1%
31.3 1323
 
< 0.1%
29.17 1318
 
< 0.1%
30.67 1314
 
< 0.1%
28.91 1314
 
< 0.1%
25.98 1314
 
< 0.1%
30.69 1314
 
< 0.1%
24.45 1309
 
< 0.1%
Other values (33277) 9986801
99.9%
ValueCountFrequency (%)
40.65 1337
 
< 0.1%
47.685 1331
 
< 0.1%
50.04 1325
 
< 0.1%
46.95 1323
 
< 0.1%
43.755 1318
 
< 0.1%
46.005 1314
 
< 0.1%
43.365 1314
 
< 0.1%
38.97 1314
 
< 0.1%
46.035 1314
 
< 0.1%
36.675 1309
 
< 0.1%
Other values (33277) 9986801
99.9%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 6
< 0.1%
0.03 5
< 0.1%
0.04 6
< 0.1%
0.05 6
< 0.1%
0.06 9
< 0.1%
0.07 9
< 0.1%
0.08 5
< 0.1%
0.09 12
< 0.1%
0.1 10
< 0.1%
ValueCountFrequency (%)
0.015 2
 
< 0.1%
0.03 6
< 0.1%
0.045 5
< 0.1%
0.06 6
< 0.1%
0.075 6
< 0.1%
0.09 9
< 0.1%
0.105 9
< 0.1%
0.12 5
< 0.1%
0.135 12
< 0.1%
0.15 10
< 0.1%
ValueCountFrequency (%)
0.015 2
 
< 0.1%
0.03 6
< 0.1%
0.045 5
< 0.1%
0.06 6
< 0.1%
0.075 6
< 0.1%
0.09 9
< 0.1%
0.105 9
< 0.1%
0.12 5
< 0.1%
0.135 12
< 0.1%
0.15 10
< 0.1%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 6
< 0.1%
0.03 5
< 0.1%
0.04 6
< 0.1%
0.05 6
< 0.1%
0.06 9
< 0.1%
0.07 9
< 0.1%
0.08 5
< 0.1%
0.09 12
< 0.1%
0.1 10
< 0.1%
 Profil - Données OriginalesProfil - Données Dérivées
Distinct731731
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size76.3 MiB76.3 MiB
 Profil - Données OriginalesProfil - Données Dérivées
Minimum2023-08-01 00:00:002023-08-01 00:00:00
Maximum2025-07-31 00:00:002025-07-31 00:00:00
Invalid dates00
Invalid dates (%)0.0%0.0%
2025-08-04T17:50:23.945275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-04T17:50:24.420953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

country_code
Categorical

 Profil - Données OriginalesProfil - Données Dérivées
Distinct77
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size562.7 MiB562.7 MiB
FR
1430346 
NG
1429600 
JP
1429313 
US
1428765 
BR
1427647 
Other values (2)
2854329 
FR
1430346 
NG
1429600 
JP
1429313 
US
1428765 
BR
1427647 
Other values (2)
2854329 

Length

 Profil - Données OriginalesProfil - Données Dérivées
Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

 Profil - Données OriginalesProfil - Données Dérivées
Total characters2000000020000000
Distinct characters1212
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Profil - Données OriginalesProfil - Données Dérivées
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Profil - Données OriginalesProfil - Données Dérivées
1st rowUSUS
2nd rowININ
3rd rowNGNG
4th rowFRFR
5th rowFRFR

Common Values

ValueCountFrequency (%)
FR 1430346
14.3%
NG 1429600
14.3%
JP 1429313
14.3%
US 1428765
14.3%
BR 1427647
14.3%
DE 1427447
14.3%
IN 1426882
14.3%
ValueCountFrequency (%)
FR 1430346
14.3%
NG 1429600
14.3%
JP 1429313
14.3%
US 1428765
14.3%
BR 1427647
14.3%
DE 1427447
14.3%
IN 1426882
14.3%

Length

2025-08-04T17:50:24.807135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Profil - Données Originales

2025-08-04T17:50:25.020559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:50:25.301226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fr 1430346
14.3%
ng 1429600
14.3%
jp 1429313
14.3%
us 1428765
14.3%
br 1427647
14.3%
de 1427447
14.3%
in 1426882
14.3%
ValueCountFrequency (%)
fr 1430346
14.3%
ng 1429600
14.3%
jp 1429313
14.3%
us 1428765
14.3%
br 1427647
14.3%
de 1427447
14.3%
in 1426882
14.3%

Most occurring characters

ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000000
100.0%
ValueCountFrequency (%)
(unknown) 20000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000000
100.0%
ValueCountFrequency (%)
(unknown) 20000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000000
100.0%
ValueCountFrequency (%)
(unknown) 20000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%
ValueCountFrequency (%)
R 2857993
14.3%
N 2856482
14.3%
F 1430346
7.2%
G 1429600
7.1%
J 1429313
7.1%
P 1429313
7.1%
U 1428765
7.1%
S 1428765
7.1%
B 1427647
7.1%
D 1427447
7.1%
Other values (2) 2854329
14.3%

customer_age
Real number (ℝ)

 Profil - Données OriginalesProfil - Données Dérivées
Distinct6262
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean48.50352248.503522
 Profil - Données OriginalesProfil - Données Dérivées
Minimum1818
Maximum7979
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size76.3 MiB76.3 MiB
2025-08-04T17:50:25.797426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 Profil - Données OriginalesProfil - Données Dérivées
Minimum1818
5-th percentile2121
Q13333
median4949
Q36464
95-th percentile7676
Maximum7979
Range6161
Interquartile range (IQR)3131

Descriptive statistics

 Profil - Données OriginalesProfil - Données Dérivées
Standard deviation17.89245217.892452
Coefficient of variation (CV)0.368889740.36888974
Kurtosis-1.2002934-1.2002934
Mean48.50352248.503522
Median Absolute Deviation (MAD)1515
Skewness-0.00041163779-0.00041163779
Sum4.8503522 × 1084.8503522 × 108
Variance320.13982320.13982
MonotonicityNot monotonicNot monotonic
2025-08-04T17:50:26.324491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 162161
 
1.6%
66 162032
 
1.6%
28 161960
 
1.6%
57 161915
 
1.6%
29 161893
 
1.6%
40 161787
 
1.6%
61 161778
 
1.6%
59 161766
 
1.6%
56 161746
 
1.6%
76 161729
 
1.6%
Other values (52) 8381233
83.8%
ValueCountFrequency (%)
71 162161
 
1.6%
66 162032
 
1.6%
28 161960
 
1.6%
57 161915
 
1.6%
29 161893
 
1.6%
40 161787
 
1.6%
61 161778
 
1.6%
59 161766
 
1.6%
56 161746
 
1.6%
76 161729
 
1.6%
Other values (52) 8381233
83.8%
ValueCountFrequency (%)
18 161095
1.6%
19 161578
1.6%
20 161547
1.6%
21 161429
1.6%
22 160302
1.6%
23 161336
1.6%
24 160626
1.6%
25 160710
1.6%
26 160595
1.6%
27 161499
1.6%
ValueCountFrequency (%)
18 161095
1.6%
19 161578
1.6%
20 161547
1.6%
21 161429
1.6%
22 160302
1.6%
23 161336
1.6%
24 160626
1.6%
25 160710
1.6%
26 160595
1.6%
27 161499
1.6%
ValueCountFrequency (%)
18 161095
1.6%
19 161578
1.6%
20 161547
1.6%
21 161429
1.6%
22 160302
1.6%
23 161336
1.6%
24 160626
1.6%
25 160710
1.6%
26 160595
1.6%
27 161499
1.6%
ValueCountFrequency (%)
18 161095
1.6%
19 161578
1.6%
20 161547
1.6%
21 161429
1.6%
22 160302
1.6%
23 161336
1.6%
24 160626
1.6%
25 160710
1.6%
26 160595
1.6%
27 161499
1.6%

payment_type
Categorical

 Profil - Données OriginalesProfil - Données Dérivées
Distinct44
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size608.0 MiB608.0 MiB
paypal
2502160 
cash
2499938 
crypto
2499192 
credit_card
2498710 
paypal
2502160 
cash
2499938 
crypto
2499192 
credit_card
2498710 

Length

 Profil - Données OriginalesProfil - Données Dérivées
Max length1111
Median length66
Mean length6.74936746.7493674
Min length44

Characters and Unicode

 Profil - Données OriginalesProfil - Données Dérivées
Total characters6749367467493674
Distinct characters1414
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Profil - Données OriginalesProfil - Données Dérivées
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Profil - Données OriginalesProfil - Données Dérivées
1st rowpaypalpaypal
2nd rowcryptocrypto
3rd rowcashcash
4th rowcryptocrypto
5th rowcryptocrypto

Common Values

ValueCountFrequency (%)
paypal 2502160
25.0%
cash 2499938
25.0%
crypto 2499192
25.0%
credit_card 2498710
25.0%
ValueCountFrequency (%)
paypal 2502160
25.0%
cash 2499938
25.0%
crypto 2499192
25.0%
credit_card 2498710
25.0%

Length

2025-08-04T17:50:26.683349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Profil - Données Originales

2025-08-04T17:50:26.859442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:50:27.037504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
paypal 2502160
25.0%
cash 2499938
25.0%
crypto 2499192
25.0%
credit_card 2498710
25.0%
ValueCountFrequency (%)
paypal 2502160
25.0%
cash 2499938
25.0%
crypto 2499192
25.0%
credit_card 2498710
25.0%

Most occurring characters

ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67493674
100.0%
ValueCountFrequency (%)
(unknown) 67493674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67493674
100.0%
ValueCountFrequency (%)
(unknown) 67493674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67493674
100.0%
ValueCountFrequency (%)
(unknown) 67493674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%
ValueCountFrequency (%)
a 10002968
14.8%
c 9996550
14.8%
p 7503512
11.1%
r 7496612
11.1%
y 5001352
7.4%
t 4997902
7.4%
d 4997420
7.4%
l 2502160
 
3.7%
s 2499938
 
3.7%
h 2499938
 
3.7%
Other values (4) 9995322
14.8%

Interactions

Profil - Données Originales

2025-08-04T17:16:11.402466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:49.407147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:15:55.753778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:32.232224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:16:03.795450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:40.941795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:16:14.005967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:52.295690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:15:58.503141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:35.318152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:16:06.290777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:43.694487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:16:16.619191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:55.146827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:16:01.146167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:38.118406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

2025-08-04T17:16:08.857301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:47:46.396824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

Profil - Données Originales

2025-08-04T17:50:27.270464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Dérivées

2025-08-04T17:50:27.658017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Profil - Données Originales

country_codecustomer_agecustomer_idpayment_typeproduct_categorypurchase_amount
country_code1.0000.0000.0000.0000.0000.000
customer_age0.0001.000-0.0000.0000.0000.000
customer_id0.000-0.0001.0000.0000.0000.000
payment_type0.0000.0000.0001.0000.0000.000
product_category0.0000.0000.0000.0001.0000.000
purchase_amount0.0000.0000.0000.0000.0001.000

Profil - Données Dérivées

country_codecustomer_agecustomer_idpayment_typeproduct_categorypurchase_amount
country_code1.0000.0000.0000.0000.0000.000
customer_age0.0001.000-0.0000.0000.0000.000
customer_id0.000-0.0001.0000.0000.0000.000
payment_type0.0000.0000.0001.0000.0000.000
product_category0.0000.0000.0000.0001.0000.000
purchase_amount0.0000.0000.0000.0000.0001.000

Missing values

Profil - Données Originales

2025-08-04T17:16:19.097787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.

Profil - Données Dérivées

2025-08-04T17:47:57.747359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.

Profil - Données Originales

2025-08-04T17:16:25.483217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Profil - Données Dérivées

2025-08-04T17:48:04.781099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Profil - Données Originales

customer_idpurchase_idproduct_categorypurchase_amountpurchase_datecountry_codecustomer_agepayment_type
08270bdd640fb-0667-4ad1-9c80-317fa3b1799delectronics26.052024-10-05US54paypal
1186023b8c1e9-3924-46de-beb1-3b9046685257fashion29.332025-03-02IN36crypto
26390bd9c66b3-ad3c-4d6d-9a3d-1fa7bc8960a9books51.172023-09-08NG75cash
36191972a8469-1641-4f82-8b9d-2434e465e150books59.312024-03-01FR66crypto
4673417fc695a-07a0-4a6e-8822-e8f36c031199sports9.382024-09-07FR75crypto
572659a1de644-815e-46d1-bb8f-aa1837f8a88bfashion33.012024-12-10NG31paypal
61466b74d0fb1-32e7-4629-8fad-c1a606cb0fb3electronics92.992023-08-14US64crypto
754266b65a6a4-8b81-48f6-b38a-088ca65ed389books32.212024-03-18US19crypto
8657847378190-96da-4dac-b2ff-5d2a386ecbe0sports24.862024-03-14US77paypal
99322c241330b-01a9-471f-9e8a-774bcf36d58belectronics24.802024-03-01NG61cash

Profil - Données Dérivées

customer_idpurchase_idproduct_categorypurchase_amountpurchase_datecountry_codecustomer_agepayment_type
08270bdd640fb-0667-4ad1-9c80-317fa3b1799delectronics39.0752024-10-05US54paypal
1186023b8c1e9-3924-46de-beb1-3b9046685257fashion43.9952025-03-02IN36crypto
26390bd9c66b3-ad3c-4d6d-9a3d-1fa7bc8960a9books76.7552023-09-08NG75cash
36191972a8469-1641-4f82-8b9d-2434e465e150books88.9652024-03-01FR66crypto
4673417fc695a-07a0-4a6e-8822-e8f36c031199sports14.0702024-09-07FR75crypto
572659a1de644-815e-46d1-bb8f-aa1837f8a88bfashion49.5152024-12-10NG31paypal
61466b74d0fb1-32e7-4629-8fad-c1a606cb0fb3electronics139.4852023-08-14US64crypto
754266b65a6a4-8b81-48f6-b38a-088ca65ed389books48.3152024-03-18US19crypto
8657847378190-96da-4dac-b2ff-5d2a386ecbe0sports37.2902024-03-14US77paypal
99322c241330b-01a9-471f-9e8a-774bcf36d58belectronics37.2002024-03-01NG61cash

Profil - Données Originales

customer_idpurchase_idproduct_categorypurchase_amountpurchase_datecountry_codecustomer_agepayment_type
999999079891ffb126f-5897-480e-b839-3a8d0035accdbooks46.152024-07-29DE76crypto
9999991232517c3e766-bf4d-4128-a509-11f4ee011ca0fashion127.412023-11-27NG22credit_card
999999275748cc4aa8f-e04b-4751-aa6f-db0f28eef8aeelectronics136.272024-05-28BR51crypto
999999393939da02928-aa42-4d2d-b6ac-803f4e6e9f4cfashion64.532024-04-19FR48credit_card
99999947615d7722a66-e9e6-4387-8c67-b9a54e4f8f1bgrocery37.192024-08-25IN68credit_card
999999523108e1c7980-cbed-4d37-831d-1cda628845ddbooks67.812025-03-07JP19crypto
99999967956f2a8821e-d63f-450d-9e5c-0a2f793c317dgrocery8.332024-05-14JP78paypal
9999997210888a0bfc1-04cc-4ff9-b8ad-756c2bbd935fsports17.892024-06-30IN79cash
99999981606ad7762d8-1892-4456-b55b-37d16adf032bsports51.302024-12-31JP46crypto
99999999666be5cc67d-d5a0-49bf-8c30-578519e7fee6fashion47.752024-02-17FR26cash

Profil - Données Dérivées

customer_idpurchase_idproduct_categorypurchase_amountpurchase_datecountry_codecustomer_agepayment_type
999999079891ffb126f-5897-480e-b839-3a8d0035accdbooks69.2252024-07-29DE76crypto
9999991232517c3e766-bf4d-4128-a509-11f4ee011ca0fashion191.1152023-11-27NG22credit_card
999999275748cc4aa8f-e04b-4751-aa6f-db0f28eef8aeelectronics204.4052024-05-28BR51crypto
999999393939da02928-aa42-4d2d-b6ac-803f4e6e9f4cfashion96.7952024-04-19FR48credit_card
99999947615d7722a66-e9e6-4387-8c67-b9a54e4f8f1bgrocery55.7852024-08-25IN68credit_card
999999523108e1c7980-cbed-4d37-831d-1cda628845ddbooks101.7152025-03-07JP19crypto
99999967956f2a8821e-d63f-450d-9e5c-0a2f793c317dgrocery12.4952024-05-14JP78paypal
9999997210888a0bfc1-04cc-4ff9-b8ad-756c2bbd935fsports26.8352024-06-30IN79cash
99999981606ad7762d8-1892-4456-b55b-37d16adf032bsports76.9502024-12-31JP46crypto
99999999666be5cc67d-d5a0-49bf-8c30-578519e7fee6fashion71.6252024-02-17FR26cash

Duplicate rows

Profil - Données Originales

customer_idpurchase_idproduct_categorypurchase_amountpurchase_datecountry_codecustomer_agepayment_type# duplicates
Dataset does not contain duplicate rows.

Profil - Données Dérivées

customer_idpurchase_idproduct_categorypurchase_amountpurchase_datecountry_codecustomer_agepayment_type# duplicates
Dataset does not contain duplicate rows.